| Graph neural ordinary differential equations M Poli, S Massaroli, J Park, A Yamashita, H Asama, J Park Workshop on Deep Learning on Graphs: Methodologies and Applications (DLGMA’20), 2019 | 390 | 2019 |
| Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning J Park, J Chun, SH Kim, Y Kim, J Park International Journal of Production Research 59 (11), 3360-3377, 2021 | 190 | 2021 |
| Physics-induced graph neural network: An application to wind-farm power estimation J Park, J Park Energy 187, 115883, 2019 | 92 | 2019 |
| Sym-nco: Leveraging symmetricity for neural combinatorial optimization M Kim, J Park, J Park Advances in Neural Information Processing Systems 35, 1936-1949, 2022 | 53 | 2022 |
| ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning J Park, S Bakhtiyar, J Park arXiv preprint arXiv:2106.03051, 2021 | 38 | 2021 |
| Wind field-based short-term turbine response forecasting by stacked dilated convolutional LSTMs S Woo, J Park, J Park, L Manuel IEEE Transactions on Sustainable Energy 11 (4), 2294-2304, 2019 | 31 | 2019 |
| Predicting wind turbine power and load outputs by multi-task convolutional LSTM model S Woo, J Park, J Park 2018 IEEE Power & Energy Society General Meeting (PESGM), 1-5, 2018 | 31 | 2018 |
| Convergent graph solvers J Park, J Choo, J Park International Conference on Learning Representations (ICLR 2022), 2021 | 18 | 2021 |
| Learning to CROSS exchange to solve min-max vehicle routing problems M Kim, J Park, J Park International Conference on Learning Representations (ICLR 2023), 2023 | 14* | 2023 |
| Learn to Solve the Min-max Multiple Traveling Salesmen Problem with Reinforcement Learning J Park, C Kwon, J Park International Conference on Autonomous Agents and Multiagent Systems (AAMAS …, 2023 | 9 | 2023 |
| RL4CO: an extensive reinforcement learning for combinatorial optimization benchmark F Berto, C Hua, J Park, M Kim, H Kim, J Son, H Kim, J Kim, J Park arXiv preprint arXiv:2306.17100, 2023 | 8 | 2023 |
| Continuous-depth neural models for dynamic graph prediction M Poli, S Massaroli, CM Rabideau, J Park, A Yamashita, H Asama, J Park arXiv preprint arXiv:2106.11581, 2021 | 6 | 2021 |
| A hypergraph convolutional neural network for molecular properties prediction using functional group F Chen, J Park, J Park arXiv preprint arXiv:2106.01028, 2021 | 5 | 2021 |
| Dissecting Neural ODEs. arXiv 2020 S Massaroli, M Poli, J Park, A Yamashita, H Asama arXiv preprint arXiv:2002.08071, 2002 | 5 | 2002 |
| Meta-sysid: A meta-learning approach for simultaneous identification and prediction J Park, F Berto, A Jamgochian, MJ Kochenderfer, J Park arXiv preprint arXiv:2206.00694, 2022 | 4 | 2022 |
| Learning context-aware adaptive solvers to accelerate quadratic programming H Jung, J Park, J Park arXiv preprint arXiv:2211.12443, 2022 | 3 | 2022 |
| Recursive Speculative Decoding: Accelerating LLM Inference via Sampling Without Replacement W Jeon, M Gagrani, R Goel, J Park, M Lee, C Lott arXiv preprint arXiv:2402.14160, 2024 | 2 | 2024 |
| A Molecular Hyper-message Passing Network with Functional Group Information F Chen, J Park, J Park arXiv preprint arXiv:2106.01028, 2021 | 2 | 2021 |
| Direct Alignment of Draft Model for Speculative Decoding with Chat-Fine-Tuned LLMs R Goel, M Gagrani, W Jeon, J Park, M Lee, C Lott arXiv preprint arXiv:2403.00858, 2024 | 1 | 2024 |
| On Speculative Decoding for Multimodal Large Language Models M Gagrani, R Goel, W Jeon, J Park, M Lee, C Lott arXiv preprint arXiv:2404.08856, 2024 | | 2024 |
Jinkyoo ParkDepartment of Industrial and Systems Engineering, KAIST在 kaist.ac.kr 的电子邮件经过验证
